Genetic history of Cambridgeshire before and after the Black Death

Urbanization and genetic homogenization in the medieval Low Countries revealed through a ten-century paleogenomic study of the city of Sint-Truiden

BACKGROUND

Processes shaping the formation of the present-day population structure in highly urbanized Northern Europe are still poorly understood. Gaps remain in our understanding of when and how currently observable regional differences emerged and what impact city growth, migration, and disease pandemics during and after the Middle Ages had on these processes.

RESULTS

We perform low-coverage sequencing of the genomes of 338 individuals spanning the eighth to the eighteenth centuries in the city of Sint-Truiden in Flanders, in the northern part of Belgium. The early/high medieval Sint-Truiden population was more heterogeneous, having received migrants from Scotland or Ireland, and displayed less genetic relatedness than observed today between individuals in present-day Flanders. We find differences in gene variants associated with high vitamin D blood levels between individuals with Gaulish or Germanic ancestry. Although we find evidence of a Yersinia pestis infection in 5 of the 58 late medieval burials, we were unable to detect a major population-scale impact of the second plague pandemic on genetic diversity or on the elevated differentiation of immunity genes.

CONCLUSIONS

This study reveals that the genetic homogenization process in a medieval city population in the Low Countries was protracted for centuries. Over time, the Sint-Truiden population became more similar to the current population of the surrounding Limburg province, likely as a result of reduced long-distance migration after the high medieval period, and the continuous process of local admixture of Germanic and Gaulish ancestries which formed the genetic cline observable today in the Low Countries.

Tracing the Evolution of Human Immunity Through Ancient DNA

Infections have imposed strong selection pressures throughout human evolution, making the study of natural selection's effects on immunity genes highly complementary to disease-focused research. This review discusses how ancient DNA studies, which have revolutionized evolutionary genetics, increase our understanding of the evolution of human immunity. These studies have shown that interbreeding between modern humans and Neanderthals or Denisovans has influenced present-day immune responses, particularly to viruses. Additionally, ancient genomics enables the tracking of how human immunity has evolved across cultural transitions, highlighting strong selection since the Bronze Age in Europe (<4,500 years) and potential genetic adaptations to epidemics raging during the Middle Ages and the European colonization of the Americas. Furthermore, ancient genomic studies suggest that the genetic risk for noninfectious immune disorders has gradually increased over millennia because alleles associated with increased risk for autoimmunity and inflammation once conferred resistance to infections. The challenge now is to extend these findings to diverse, non-European populations and to provide a more global understanding of the evolution of human immunity.

Estimating effective population size trajectories from time-series identity-by-descent segments

Long, identical haplotypes shared between pairs of individuals, known as identity-by-descent (IBD) segments, result from recently shared co-ancestry. Various methods have been developed to utilize IBD sharing for demographic inference in contemporary DNA data. Recent methodological advances have extended the screening for IBD segments to ancient DNA (aDNA) data, making demographic inference based on IBD also possible for aDNA. However, aDNA data typically have varying sampling times, but most demographic inference methods for modern data assume that sampling is contemporaneous. Here, we present Ttne (Time-Transect Ne), which models time-transect sampling to infer recent effective population size trajectories. Using simulations, we show that utilizing IBD sharing in time series increased resolution to infer recent fluctuations in effective population sizes compared with methods that only use contemporaneous samples. To account for IBD detection errors common in empirical analyses, we implemented an approach to estimate and model IBD detection errors. Finally, we applied Ttne to two aDNA time transects: individuals associated with the Copper Age Corded Ware Culture and Medieval England. In both cases, we found evidence of a growing population, a signal consistent with archaeological records.

Deep estimation of the intensity and timing of natural selection from ancient genomes

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.

Health inequality in medieval Cambridge, 1200-1500 CE

Health inequality is not only a major problem today; it left its mark upon past societies too. For much of the past, health inequality has been poorly studied, mostly because bioarchaeologists have concentrated upon single sites rather than a broader social landscape. This article compares 476 adults in multiple locations of medieval Cambridge (UK). Samples include ordinary townspeople (All Saints), people living in a charitable institution (the Hospital of St. John), and members of a religious order (the Augustinian Friary). These groups shared many conditions of life, such as a similar range of diseases, risk of injury, and vertebral disk degeneration. However, people living on charity had more indicators of poor childhood health and diet, lower adult stature, and a younger age at death, reflecting the health effects of poverty. In contrast, the Augustinian friars were members of a prosperous, well-endowed religious house. Compared with other groups, they were taller (perhaps a result of a richer diet during their adolescent growth period); their adult carbon and nitrogen isotope values are higher, suggesting a diet higher in terrestrial and/or marine animal protein; and they had the highest prevalence of foot problems related to fashionable late medieval footwear. As this illustrates, health inequality will take particular forms depending upon the specificities of a social landscape; except in unusual circumstances where a site and its skeletal samples represent a real cross-section of society, inequality is best investigated by comparison across sites.

READv2: advanced and user-friendly detection of biological relatedness in archaeogenomics

The advent of genome-wide ancient DNA analysis has revolutionized our understanding of prehistoric societies. However, studying biological relatedness in these groups requires tailored approaches due to the challenges of analyzing ancient DNA. READv2, an optimized Python3 implementation of the most widely used tool for this purpose, addresses these challenges while surpassing its predecessor in speed and accuracy. For sufficient amounts of data, it can classify up to third-degree relatedness and differentiate between the two types of first-degree relatedness, full siblings and parent-offspring. READv2 enables user-friendly, efficient, and nuanced analysis of biological relatedness, facilitating a deeper understanding of past social structures.

Unveiling recent and ongoing adaptive selection in human populations

Genome-wide scans for signals of selection have become a routine part of the analysis of population genomic variation datasets and have resulted in compelling evidence of selection during recent human evolution. This Essay spotlights methodological innovations that have enabled the detection of selection over very recent timescales, even in contemporary human populations. By harnessing large-scale genomic and phenotypic datasets, these new methods use different strategies to uncover connections between genotype, phenotype, and fitness. This Essay outlines the rationale and key findings of each strategy, discusses challenges in interpretation, and describes opportunities to improve detection and understanding of ongoing selection in human populations.